Characterization of bedrock mass-wasting at fault-bound abyssal hills
Bibliographic record
Abstract
• Bedrock mass wasting on abyssal hills shapes scarp morphology from fault inception. • Earthquakes are a key driver of bedrock mass wasting on abyssal hills. • Ratio of mass removal efficiency to uplift rate is similar across spreading rates. • Global earthquake-driven mass flux for abyssal hills between 24 and 1428 m m 3 yr −1 . Fault-bound abyssal hills form at mid-ocean ridges and cover ∼65 % of Earth's surface, but few studies have characterized the extent to which bedrock erosion controls their morphology. Here, we use bathymetry data to characterize the morphology of fault-bound abyssal hills on a global scale, and employ numerical modelling and seismicity catalogues to quantify how simultaneous rock uplift and bedrock erosion sculpt deep-ocean landscapes. By generating a global database on abyssal hill morphology, we show that most large abyssal-hill scarps (>100 m in height) within the near-axis zone of seismicity (i.e., <30 km from axis) have slopes between 10 and 30°, well below the expected range of underlying normal fault dips of 45–60°. We interpret this as a manifestation of efficient bedrock mass wasting on near-axis growing faults, a process that operates from fault inception. Using a non-linear topographic diffusion model to parameterise the effects of erosion, we find a balance between erosion and rock uplift that is similar for slow, intermediate, and fast spreading rates. We express the ratio of erosion to uplift as an inverse Peclet number that ranges between 0.06 and 0.82 for abyssal hills. We also calculate a global bedrock diffusivity for abyssal hills in the range 0.01–1.51 m 2 yr −1 . These results imply that bedrock erosion is a significant process that sculpts abyssal hill morphology and reshapes the oceanic crust. Overall, this study provides a framework to incorporate bedrock mass wasting into future models of ocean-floor evolution and, more generally, to active extensional settings on Earth and beyond.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".